Autenticação Biométrica Baseada em PPG e ECG utilizando Aprendizado Profundo
Resumo
A popularização da Internet das Coisas aumentou significativamente os requisitos para a transmissão e armazenamento de dados pessoais sensíveis. Consequentemente, esses avanços exigem políticas rígidas de controle de acesso com a necessidade de garantir segurança e privacidade de forma eficaz. É possível encontrar na literatura que a autenticação biométrica baseada em sinais de PPG (fotopletismografia) ou ECG (eletrocardiograma) são potenciais suportes no atendimento a esses requisitos. Pensando nisso, este artigo propõe um método de identificação multimodal de indivíduos, combinando ambos os sinais. Nossa proposta combina duas redes neurais convolucionais em cascata, dando avanços ao estado da arte. Como resultados numéricos, o método atinge 99,62% de acurácia, 93,83% de precisão e 0,04% FAR em diferentes bases de dados.
Palavras-chave:
Autenticação Biométrica, Sinais Biométricos, ECG, PPG, Aprendizado Profundo
Referências
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M. M. Dhanvijay and S. C. Patil, "Internet of things: A survey of enabling technologies in healthcare and its applications" Computer Networks, vol. 153, pp. 113--131, 2019.
K. K. Coelho, M. Nogueira, M. C. Marim, E. F. Silva, A. B. Vieira, and J. A. M. Nacif, Lorena: Low memory symmetric-key generation method for based on group cryptography protocol applied to the internet of healthcare things" IEEE Access, vol. 10, pp. 12 564--12 579, 2022.
K. Coelho, D. Damião, G. Noubir, A. Borges, M. Nogueira, and J. Nacif, "Cryptographic algorithms in wearable communications: An empirical analysis" IEEE Communications Letters, vol. 23, no. 11, pp. 1931--1934, 2019.
W. Yang, S. Wang, N. M. Sahri, N. M. Karie, M. Ahmed, and C. Valli, "Biometrics for internet-of-things security: A review" Sensors, vol. 21, no. 18, 2021. [Online]. Available: https://www.mdpi.com/1424-8220/21/18/6163
E. Cerqueira, P. Resque, I. Medeiros, L. Bastos, A. Santos, T. Tavares, D. Rosário, A. Santos, and M. Nogueira, "Autenticação usando sinais biométricos: Fundamentos, aplicações e desafios" Sociedade Brasileira de Computação, 2019.
A. S. Rathore, Z. Li, W. Zhu, Z. Jin, and W. Xu, "A survey on heart biometrics" ACM Comput. Surv., vol. 53, no. 6, dec 2020.
L. Everson, D. Biswas, M. Panwar, D. Rodopoulos, A. Acharyya, C. H. Kim, C. Van Hoof, M. Konijnenburg, and N. Van Helleputte, "Biometricnet: Deep learning based biometric identification using wrist-worn ppg" in 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 2018, pp. 1--5.
D. Biswas, L. Everson, M. Liu, M. Panwar, B.-E. Verhoef, S. Patki, C. H. Kim, A. Acharyya, C. Van Hoof, M. Konijnenburg, and N. Van Helleputte, "Cornet: Deep learning framework for ppg-based heart rate estimation and biometric identification in ambulant environment" IEEE Transactions on Biomedical Circuits and Systems, vol. 13, no. 2, pp. 282--291, 2019.
J. Yang, Y. Huang, F. Huang, and G. Yang, "Photoplethysmography biometric recognition model based on sparse softmax vector and k-nearest neighbor"Journal of Electrical and Computer Engineering, vol. 2020, p. 9653470, Oct 2020.
J. Yang, Y. Huang, R. Zhang, F. Huang, Q. Meng, and S. Feng, "Study on ppg biometric recognition based on multifeature extraction and naive bayes classifier" Scientific Programming, vol. 2021, p. 5597624, May 2021.
C. Liu, Y. Huang, F. Huang, and J. Yu, "Multifeature deep cascaded learning for ppg biometric recognition" Scientific Programming, vol. 2022, p.7477746, Mar 2022.
Y. Li, Y. Pang, K. Wang, and X. Li, "Toward improving ecg biometric identification using cascaded convolutional neural networks" Neurocomputing, vol. 391, pp. 83--95, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231220300485.
M. Hammad, S. Zhang, and K. Wang, "A novel two-dimensional ecg feature extraction and classification algorithm based on convolution neural network for human authentication" Future Generation Computer Systems, vol. 101, pp. 180--196, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167739X18329923
C. Yaacoubi, R. Besrour, and Z. Lachiri, "A multimodal biometric identification system based on ecg and ppg signals" in Proceedings of the 2nd International Conference on Digital Tools &Uses Congress, ser. DTUC '20. New York, NY, USA: Association for Computing Machinery, 2020. [Online]. Available: https://doi.org/10.1145/3423603.3424053
S. Bianco and P. Napoletano, "Biometric recognition using multimodal physiological signals" IEEE Access, vol. 7, pp. 83 581--83 588, 2019.
R. Donida Labati, E. Muñoz, V. Piuri, R. Sassi, and F. Scotti, "Deep-ecg: Convolutional neural networks for ecg biometric recognition" Pattern Recognition Letters, vol. 126, pp. 78--85, 2019, robustness, Security and Regulation Aspects in Current Biometric Systems. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167865518301077
Z. Zhang, Z. Pi, and B. Liu, "TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise" IEEE Transactions on Biomedical Engineering, vol. 62, no. 2, pp. 522--531, feb 2015. [Online]. Available: https://doi.org/10.1109%2Ftbme.2014.2359372
B. L. Zhilin Zhang, Zhouyue Pi, "Ieee signal processing cup 2015: Heart rate monitoring during physical exercise using wrist-type photoplethysmographic (ppg) signals" https://web.archive.org/web/20201009233959/ https://sites.google.com/site/researchbyzhang/ieeespcup2015 august 2014, acessado em 05/2022.,
W. Karlen, "CapnoBase IEEE TBME Respiratory Rate Benchmark" 2021. [Online]. Available: https://doi.org/10.5683/SP2/NLB8IT
M. A. F. Pimentel, A. E. W. Johnson, P. H. Charlton, D. Birrenkott, P. J. Watkinson, L. Tarassenko, and D. A. Clifton, "Toward a robust estimation of respiratory rate from pulse oximeters" IEEE Transactions on Biomedical Engineering, vol. 64, no. 8, pp. 1914--1923, 2017.
L. Bastos, T. Tavares, D. Rosário, E. Cerqueira, A. Santos, and M. Nogueira, "Double authentication model based on ppg and ecg signals" in 2020 International Wireless Communications and Mobile Computing (IWCMC), 2020, pp. 601--606.
L. D. L. BASTOS, "Sistema de identificacao duplo (sid) de usuarios atraves dos biosinais fotopletismograma e eletrocardiograma" Master's thesis, Universidade Federal do Pará, 2020.
H. P. da Silva, A. Lourenço, A. Fred, N. Raposo, and M. A. de Sousa, "Check your biosignals here: A new dataset for off-the-person ecg biometrics" Computer Methods and Programs in Biomedicine, vol. 113, no. 2, pp. 503--514, 2014. [Online]. Available: [link]
R. Bousseljot, D. Kreiseler, and A. Schnabel, "Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das internet" pp. 317--318, Jan. 1995.
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, ËTMI. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors, "SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python" Nature Methods, vol. 17, pp. 261--272, 2020.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al., "{TensorFlow}: a system for {Large-Scale} machine learning" in 12th USENIX symposium on operating systems design and implementation (OSDI 16), 2016, pp. 265--283.
F. Chollet et al., "Keras" 2015. [Online]. Available: https://github.com/fchollet/keras
W. McKinney et al., "Data structures for statistical computing in python" in Proceedings of the 9th Python in Science Conference, vol. 445. Austin, TX, 2010, pp. 51--56.
C. R. Harris, K. J. Millman, S. J. Van Der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith et al., "Array programming with numpy" Nature, vol. 585, no. 7825, pp. 357–362, 2020.
Publicado
21/11/2022
Como Citar
TRISTÃO, Eduardo T.; COELHO, Kristtopher K.; SILVA, Edelberto Franco; VIEIRA, Alex B.; NOGUEIRA, Michele; NACIF, José Augusto M..
Autenticação Biométrica Baseada em PPG e ECG utilizando Aprendizado Profundo. In: ARTIGOS COMPLETOS - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 12. , 2022, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 25-31.
ISSN 2763-9002.
DOI: https://doi.org/10.5753/sbesc_estendido.2022.227273.
